Customer Experience Management Starts with Effective Customer Data Analysis

Person looking at graphs on laptop

Successful Customer Experience Management (CEM) is data-led, and customer data analysis is its foundational activity.

Without a continuous analytics framework that informs the enterprise as to how customers both navigate and perceive your offering, the opportunity to design a great Customer Experience (CX) will not materialize. Furthermore, when relying solely on data sources that do not directly include the perception of the experience from the customer’s point-of-view, there is a risk that design efforts will optimize something other than the customer experience.

Effective customer data analytics informs an enterprise as to how they can deliver a strong customer experience, enhance its competitive position and elevate the cost/value equation of its solutions through the following ways:

Cost-Side Value-Side

Eliminate customer friction and redundancy through the design of more satisfying customer interactions.


Increase renewal, upsell, generate new leads through positive word-of-mouth by creating satisfying customer experiences.

The volume of data available at CX practitioners’ disposal has increased dramatically. Customer Relationship Management (CRM) data, customer behavioral / web analytics data, transactional data, Voice of the Customer (VoC) data, customer success applications, content marketing, digital marketing – the list of data sources is endless.

This growth has fueled both the opportunity and the complexity surrounding CEM. However, the key to world-class CX design is not just in the collection and storage of data; Solid data integration, interpretation, and application is the necessity and competitive advantage for any enterprise competing in customer experience.

A strong customer data analytics foundation that supports effective customer experience management requires some foundational principles on which to extract the right insights and actions.

Secondary data alone is not enough for effective customer data analysis

Any system or entity that interacts with the customer can generate data, and often, each system purports to offer insights that can manage the customer experience. While this is true, it is important to not rely solely on secondary data, but instead on multiple data sources to confirm a hypothesis or an initiative for customer experience design.

Secondary data is data that is generated from a system for which its main purpose is not necessarily to collect customer data solely for insight.

For example, the main purpose of CRM systems is to manage the sales process, and the characteristics of the data that these systems collect are oriented towards that objective. The values inputted into the CRM system and the categories that are assigned are all, first and foremost, focused on optimizing the sales process, but not necessarily to optimize the customer experience itself.

The same is true for other systems. Content marketing tools, ad tools, customer success tools, support tools – the data that is valuable to CX design is secondary to these systems’ primary purpose, and the interpretation of their data must always keep in mind these purposes. However, when the data set is extracted from a system for analysis, the source, and any bias within it, are sometimes forgotten, and the data may be treated as representative and unbiased as a result.

Assumption is the enemy of customer data analytics.

Relying solely on standalone sources of data for your CEM efforts, particularly secondary data that does not account for how the customer perceived their experience, may increase the risk of developing initiatives based only on how you think your customers view their experience. As a result, CX initiatives could drift from their desired effect.

Voice of the Customer (VoC) is a key customer data source in CEM

An experience is something that is perceived by a customer during a moment of interaction. As such, when it comes to the analysis and management of the customer experience, the most important customer analytics data a CX practitioner can have is Voice of the Customer (VoC) data.

VoC is primary data – it is collected solely to determine the customers’ perception of the experience (along with other customer-led values), as well as to measure and improve the customer experience. It is a moment to check in as the customer engages with the other components of the overall value you deliver through your products and processes.

The feedback collected, whether using customer surveys or direct feedback tools, is as close as it can be to the customer’s perception of their experience (they are directly providing you with feedback about their experience). It is also close to the moment of the experience, since it is typically collected during or immediately following the experience.

Your customers’ perception of their experience is a perceived state based on how the experience succeeded in meeting their needs. It is shaped by the intent of their interaction, as well as the expectations they have established through previous interactions with your company or others. The perception of the experience affects the next step in the customer journey, and whether that will be positive or negative for the business.

A customer’s intent or sentiment can only be confirmed by the customer itself. As such, you cannot manage the Customer Experience without these insights.

In reference to the previous section, each system aims to optimize different desirable outcomes using their respective data sets. However, if these different systems’ data sets are leveraged separately from one another, especially without taking customer feedback into account, there is a potential risk of creating a worse overall experience along the customer’s journey, especially as they navigate different touchpoints.

Integrating VoC with other data sets better aligns your CX efforts

Customer data analytics is, by necessity, an integration of multiple data sources. Integrating the primary data from your VoC efforts with the secondary data from your other data sources adds the crucial context needed to better understand your customers’ behavior.

Without VoC integrated into your other data sources, there is a risk of building processes that will only optimize business outcomes, instead of optimizing the experience as perceived by the customer.

Imagine you have a web application built to provide your customers with a service as part of your overall offering. You could assume, without much argument from anyone, that frequency of login to your application is a good indicator of your users’ engagement and, therefore, satisfaction with your platform. However, leveraging VoC and injecting the actual customer perception with other customer data systems, such as web analytics or session replay, may show that a significant segment demonstrates satisfaction, despite having low login rates.

Deeper analysis may uncover a very satisfied segment that does not log in frequently but is receiving detailed push reports that they rave about. These users could be urged to login to increase engagement scores by limiting the detail in their push reports, but that is unlikely to create increased satisfaction. In fact, CX design may proactively begin to offer push notifications to users who are regularly logging in to view the same thing.

Without integrated VoC data that provides the perception of the experience, the assumption about the positivity of “increased logins” would go unchallenged. Solutions to increase this KPI may start to deteriorate the customer’s perception of the experience, while the enterprise celebrates their increased rates of engagement.

Overall, the solution for effective customer data analytics, as it relates to your CEM efforts, is to add an overall layer of customer experience to your data sets, driven by the one KPI all systems should be contributing to improve; the customer perception of the experience.

Look at “success” from a customer’s perspective

Modern customer data analytics is increasingly being driven by machine learning approaches that affect the individual experience in real-time. In many cases, these AI-anticipatory algorithms leverage data to train against examples of customer success, continuously optimizing the experience at an individual level.

What is often overlooked is the choices being made as to what is an example of success. Clicks, conversions, scrolls, even purchases optimize on business success, but not necessarily to the benefit of the customer experience. A purchase might not represent the fulfillment of the customer need if they, in fact, wanted multiple items to complete the solution they had in mind.

Success in CX is a well-perceived customer experience that is efficient and leads to the right next step in the customer journey.

That success encompasses all the clicks, calls, scrolls, conversions and purchases that led to that sentiment of satisfaction. These elements, which can often be considered successful outcomes on their own, are in fact attributes to be leveraged to model a successful experience. This is why the integration of VoC data is so fundamental to the appropriate execution of customer data analytics, including machine learning opportunities.

Don’t assume what success looks like for your customer. Let the direct VoC data tell you.

Ask your customers, and then build an analysis that explains that success, or the machine learning models that train on that success. Demand that your system algorithms include the perception of the experience to ensure not only business success, but also success in the eyes of your customers.

Customer data analytics has become the key competency for successful CEM design, and when done effectively, it provides the intelligence that translates customer data into strategic direction, effective experience design, and the anticipatory execution that will both scale and individualize successful experiences.

This article was originally published on, and written by Lane Cochrane, Chief Innovation Officer at iperceptions.